Double hierarchical generalized linear models

By Youngjo Lee, John A. Nelder. 2006

110 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a5686b5f596810fcedb9e954814e96638511bc88

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Citations in the corpus

Conditional and Marginal Models: Another View (2004, methodology): “Lee and Nelder (2003a) discussed invariance associated with h-likelihood, and Noh and Lee (2003) gave numerical evidence to show that it outperforms all the other Laplace-approximation-based alternatives in estimation of GLMMs for binary data.”
Modeling Heterogeneous Variance–Covariance Components in Two-Level Models (2014, methodology): “…article is to review, describe, and illustrate a set of recent extensions that have been proposed in the literature for modeling and testing hypotheses about the sources of variance–covariance heterogeneity in multilevel models (e.g., Hedeker, Mermelstein, & Demirtas, 2008; Lee & Nelder, 2006).”
Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method. (2019, methodology): “13 For fitting generalized linear model (GLM) (Lee and Nelder, 2006), GLMBoost generally uses a 264 functional gradient descent method for optimizing the overall loss functions through implementing 265 component-wise least squares while the variable selection can be carried out simultaneously 266 (B”
Kriging a soil variable with a simple nonstationary variance model (2009, background): “There are various other examples of studies where the variance of the random component of a linear model is modeled as a function of some covariate so as to avoid assumptions of homogeneity (e.g., Verbyla 1993; Pourahmadi 2000; Al-Rawwash and Pourahmadi 2006; Lee and Nelder 2006).”